{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5rmXS40RmJJ8"
   },
   "source": [
    "# Installation & Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H-qinm2JlTix",
    "outputId": "1c81a04f-379b-4b28-8b98-f1fb1d3f790c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.28.0\n",
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      "Installing collected packages: evaluate\n",
      "Successfully installed evaluate-0.4.0\n",
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "git-lfs is already the newest version (2.9.2-1).\n",
      "0 upgraded, 0 newly installed, 0 to remove and 24 not upgraded.\n",
      "4.28.0\n"
     ]
    }
   ],
   "source": [
    "#install\n",
    "!pip install transformers==4.28.0  # due to issue https://github.com/huggingface/transformers/issues/22816\n",
    "!pip install datasets\n",
    "\n",
    "!pip install evaluate\n",
    "!apt install git-lfs\n",
    "import transformers\n",
    "print(transformers.__version__)\n",
    "from google.colab import drive\n",
    "from transformers import AutoTokenizer, DataCollatorWithPadding, pipeline\n",
    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
    "import numpy as np\n",
    "import datasets\n",
    "import evaluate\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9IZBSSYYmHmG",
    "outputId": "5b8a59fd-b677-4fe5-dfb8-6c7f1335a65a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/gdrive/\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "drive.mount('/content/gdrive/', force_remount=True)\n",
    "\n",
    "data_dir = '/content/gdrive/path/to/data'\n",
    "\n",
    "# input file: hydrated Tweets\n",
    "# format: parquet\n",
    "# columns: tweet, is_misinfo\n",
    "# index = id\n",
    "ds2_fp = os.path.join(data_dir, 'antivax.parquet')\n",
    "\n",
    "# output dirs\n",
    "dataset_dir = os.path.join(data_dir, 'vaccine_ds')\n",
    "model_dir = os.path.join(data_dir, 'vaxx_bert_model')\n",
    "os.path.exists(data_dir )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U0RgsakYmOoT"
   },
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fqowEbQu2WuX"
   },
   "outputs": [],
   "source": [
    "col_text, col_label, col_label_orig = 'after_lemmatization', 'label', 'is_misinfo'  # 'tweet'\n",
    "cols_orig = [col_text, col_label_orig]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "c64em_FEI547"
   },
   "outputs": [],
   "source": [
    "from datasets import DatasetDict, Dataset\n",
    "def create_dataset():\n",
    "  ds = DatasetDict()\n",
    "  df = pd.read_parquet(ds2_fp)\n",
    "  df = df[cols_orig]\n",
    "  df = df[~df.index.duplicated(keep='first')]\n",
    "  df.rename(columns={col_label_orig:col_label}, inplace=True)\n",
    "\n",
    "  print(df.head())\n",
    "  print('label values', df[col_label].value_counts())\n",
    "\n",
    "  df1 = df[df[col_label] == 0]\n",
    "  df2 = df[df[col_label] == 1]\n",
    "  assert len(df1.index) > .3 * len(df2.index)\n",
    "  #breakpoint()\n",
    "  train_frac = .85\n",
    "  df1i = df1.sample(frac=train_frac, random_state=1)\n",
    "  df2i = df2.sample(frac=train_frac, random_state=1)\n",
    "  df_train = pd.concat((df1i, df2i), axis=0)\n",
    "  df_test = df[~df.index.isin(df_train.index)]\n",
    "  print('train labels', df_train[col_label].value_counts())\n",
    "  print('test labels', df_test[col_label].value_counts())\n",
    "  ds['train'] = Dataset.from_pandas(df_train)\n",
    "  ds['test'] = Dataset.from_pandas(df_test)\n",
    "  return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 570,
     "referenced_widgets": [
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      "6ce20a0e672e4c8ca46afeb857cefa02",
      "05084abe764c4843881e29dd82d2221c",
      "50188fe9305b4ca494ce8c77e71daf7c",
      "998857bc44af478baef85222a4cd7855",
      "24654c14d33647e681914719654652d7",
      "43139c421e41426c8b6965d0260a5d30",
      "a77a93519c4f4bc09181300903220099"
     ]
    },
    "id": "vsTKTvFBoUPQ",
    "outputId": "d7401558-0b72-4bef-c6f5-438546709faf",
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "# dataset format:\n",
    "# {\"test\": [{\"label\": 0, \"text\": \"blabla\"}, …], \"train: …\"\n",
    "import pandas as pd\n",
    "\n",
    "ds = create_dataset()\n",
    "ds.save_to_disk(dataset_dir)\n",
    "ds = ds.shuffle(seed=42)\n",
    "print(ds)\n",
    "print('train', ds['train'][0])\n",
    "print('train', ds['train'][-1])\n",
    "print('test', ds['test'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "QkFh7r6SmTVl",
    "outputId": "1a082ed0-eef2-488e-ce66-360cb5cd5a93"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f006e9fe74ad4bc4bc0f4ef7450014a6"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "420120352a194bc4ace9d8286f0ec94d"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "76ad091be4054542904ab48d51ed5c48"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "af6af20bf2044e76bc41d29cd8305c58"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/11990 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e9b6556eb67040b98bc8614d5107634b"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Map:   0%|          | 0/2116 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e998b4c280ce48fb87b8fa11a5a86062"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples[col_text], truncation=True)\n",
    "\n",
    "tokenized_ds = ds.map(preprocess_function, batched=True) # same as spencer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0NjM5tphm0Su"
   },
   "outputs": [],
   "source": [
    "# efficient to dynamically pad the sentences to the longest length in a batch during collation\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer) # same as spencer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FfHaurt5nJIm"
   },
   "source": [
    "# Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gBbYSRuSnK1i",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "d6e525da1d1d484d9c251bc612dba0d4",
      "e7fd60007e4c4e458a5cb0de1d8e270a",
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      "a88aa7bef0a545ea807aa941f4ac65f3",
      "d9e0476358f94439941fb3d1e73118c5",
      "412957ba1f02449495c8af670e1e3e72"
     ]
    },
    "outputId": "6ac7445c-4d4c-4ee1-9dc7-921b9ee50084"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d6e525da1d1d484d9c251bc612dba0d4"
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "accuracy = evaluate.load(\"accuracy\")\n",
    "\n",
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    return accuracy.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d60cnhhPn6T7"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nvFNut3-mUT9"
   },
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "mr9Y-y_YmWpy"
   },
   "outputs": [],
   "source": [
    "id2label = {0: \"no_misinfo\", 1: \"is_misinfo\"}  # or LABEL_0 and LABEL_1\n",
    "label2id = {\"no_misinfo\": 0, \"is_misinfo\": 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161,
     "referenced_widgets": [
      "569e54c74cde4d408123277cb088ac55",
      "116d84ce2d674a4c9c3d651d396ffe11",
      "7b2d0b26e7114ba19810836b52ef93a0",
      "2566aa22cdba490a817dc7f06b039f71",
      "3b2a97225bda48b19b70846942964bc0",
      "eee8bc336f9c46e88118adfadbf523e6",
      "f8cff2c017cc4b71a3082bce55ad33c3",
      "170b8f4bd6dd4039bd8535bfb0c5d0f9",
      "76604f5a3a1141daa740d77d243deeca",
      "f61d16f84e814253a41dab2dc94f0360",
      "91c0f7fe955142488853a30864d2ad1e"
     ]
    },
    "id": "eR-RUwclnaLp",
    "outputId": "f842ccbb-8f14-4d6d-f065-45895e98c629"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "569e54c74cde4d408123277cb088ac55"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_transform.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight']\n",
      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"distilbert-base-uncased\", num_labels=2, id2label=id2label, label2id=label2id\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vroFNIew6jeW"
   },
   "outputs": [],
   "source": [
    "#!pip install --upgrade accelerate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cl42EKSVr9IE"
   },
   "source": [
    "model for corona vaccines https://drive.google.com/file/d/1QW9D6TN4KXX6poa6Q5L6FVgqaDQ4DxY9/view\n",
    "uses 3 epochs and 4392 optimization steps.\n",
    "\n",
    "Parameters from https://huggingface.co/docs/transformers/v4.17.0/en/custom_datasets, I use 3 instead of 5 epochs\n",
    "\n",
    "spencer uses arguments as here: training_args = TrainingArguments(\"test-trainer\")"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(TrainingArguments(\"test-trainer\"))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M5vCZt6y67p3",
    "outputId": "225cefdf-c011-4263-b3ac-f1c5b57f504e"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "TrainingArguments(\n",
      "_n_gpu=0,\n",
      "adafactor=False,\n",
      "adam_beta1=0.9,\n",
      "adam_beta2=0.999,\n",
      "adam_epsilon=1e-08,\n",
      "auto_find_batch_size=False,\n",
      "bf16=False,\n",
      "bf16_full_eval=False,\n",
      "data_seed=None,\n",
      "dataloader_drop_last=False,\n",
      "dataloader_num_workers=0,\n",
      "dataloader_pin_memory=True,\n",
      "ddp_bucket_cap_mb=None,\n",
      "ddp_find_unused_parameters=None,\n",
      "ddp_timeout=1800,\n",
      "debug=[],\n",
      "deepspeed=None,\n",
      "disable_tqdm=False,\n",
      "do_eval=False,\n",
      "do_predict=False,\n",
      "do_train=False,\n",
      "eval_accumulation_steps=None,\n",
      "eval_delay=0,\n",
      "eval_steps=None,\n",
      "evaluation_strategy=no,\n",
      "fp16=False,\n",
      "fp16_backend=auto,\n",
      "fp16_full_eval=False,\n",
      "fp16_opt_level=O1,\n",
      "fsdp=[],\n",
      "fsdp_config={'fsdp_min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False},\n",
      "fsdp_min_num_params=0,\n",
      "fsdp_transformer_layer_cls_to_wrap=None,\n",
      "full_determinism=False,\n",
      "gradient_accumulation_steps=1,\n",
      "gradient_checkpointing=False,\n",
      "greater_is_better=None,\n",
      "group_by_length=False,\n",
      "half_precision_backend=auto,\n",
      "hub_model_id=None,\n",
      "hub_private_repo=False,\n",
      "hub_strategy=every_save,\n",
      "hub_token=<HUB_TOKEN>,\n",
      "ignore_data_skip=False,\n",
      "include_inputs_for_metrics=False,\n",
      "jit_mode_eval=False,\n",
      "label_names=None,\n",
      "label_smoothing_factor=0.0,\n",
      "learning_rate=5e-05,\n",
      "length_column_name=length,\n",
      "load_best_model_at_end=False,\n",
      "local_rank=-1,\n",
      "log_level=passive,\n",
      "log_level_replica=warning,\n",
      "log_on_each_node=True,\n",
      "logging_dir=test-trainer/runs/May23_00-39-02_4bf3b595ef41,\n",
      "logging_first_step=False,\n",
      "logging_nan_inf_filter=True,\n",
      "logging_steps=500,\n",
      "logging_strategy=steps,\n",
      "lr_scheduler_type=linear,\n",
      "max_grad_norm=1.0,\n",
      "max_steps=-1,\n",
      "metric_for_best_model=None,\n",
      "mp_parameters=,\n",
      "no_cuda=False,\n",
      "num_train_epochs=3.0,\n",
      "optim=adamw_hf,\n",
      "optim_args=None,\n",
      "output_dir=test-trainer,\n",
      "overwrite_output_dir=False,\n",
      "past_index=-1,\n",
      "per_device_eval_batch_size=8,\n",
      "per_device_train_batch_size=8,\n",
      "prediction_loss_only=False,\n",
      "push_to_hub=False,\n",
      "push_to_hub_model_id=None,\n",
      "push_to_hub_organization=None,\n",
      "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
      "ray_scope=last,\n",
      "remove_unused_columns=True,\n",
      "report_to=['tensorboard'],\n",
      "resume_from_checkpoint=None,\n",
      "run_name=test-trainer,\n",
      "save_on_each_node=False,\n",
      "save_safetensors=False,\n",
      "save_steps=500,\n",
      "save_strategy=steps,\n",
      "save_total_limit=None,\n",
      "seed=42,\n",
      "sharded_ddp=[],\n",
      "skip_memory_metrics=True,\n",
      "tf32=None,\n",
      "torch_compile=False,\n",
      "torch_compile_backend=None,\n",
      "torch_compile_mode=None,\n",
      "torchdynamo=None,\n",
      "tpu_metrics_debug=False,\n",
      "tpu_num_cores=None,\n",
      "use_ipex=False,\n",
      "use_legacy_prediction_loop=False,\n",
      "use_mps_device=False,\n",
      "warmup_ratio=0.0,\n",
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     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
      "  warnings.warn(\n",
      "You're using a DistilBertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
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    "training_args = TrainingArguments(\n",
    "    output_dir=model_dir,\n",
    "    learning_rate=2e-5,  #default 5e-05,\n",
    "    per_device_train_batch_size=16,  # default 8\n",
    "    per_device_eval_batch_size=16,  # default 8\n",
    "    num_train_epochs=3, # default = 3, examples uses 5\n",
    "    weight_decay=0.01,  # default = 0\n",
    "    evaluation_strategy=\"steps\",  #default =no,\n",
    "    eval_steps=500,  # default None\n",
    "    load_best_model_at_end=True,\n",
    "    save_total_limit=2,  # default None\n",
    "    push_to_hub=False,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_ds[\"train\"],\n",
    "    eval_dataset=tokenized_ds[\"test\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "\n",
    "trainer.train() # resume_from_checkpoint = True -> change path above\n",
    "trainer.save_model() # saves best model to model_dir, Means that the last saved checkpoint is the checkpoint with the best validation score instead of the final weights if I am saving multiple checkpoints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Use it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "kisT0Vlpn9uw"
   },
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   "source": [
    "\n",
    "text =  'RT: Vaccine kills people!'\n",
    "classifier = pipeline(\"sentiment-analysis\", model=model_dir)\n",
    "classifier(text)"
   ]
  }
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